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We have developed several autotuning benchmarks in CUDA that take intoaccount performance-relevant source-code parameters and reach nearpeak-performance on various GPU architectures. We have used them during thedevelopment and evaluation of a novel search method for tuning space proposedin [1]. With our framework Kernel Tuning Toolkit, freely available at Github,we measured computation times and hardware performance counters on several GPUsfor the complete tuning spaces of five benchmarks. These data, which we providehere, might benefit research of search algorithms for the tuning spaces of GPUcodes or research of relation between applied code optimization, hardwareperformance counters, and GPU kernels' performance.  Moreover, we describe the scripts we used for robust evaluation of oursearcher and comparison to others in detail. In particular, the script thatsimulates the tuning, i.e., replaces time-demanding compiling and executing thetuned kernels with a quick reading of the computation time from our measureddata, makes it possible to inspect the convergence of tuning search over alarge number of experiments. These scripts, freely available with our othercodes, make it easier to experiment with search algorithms and compare them ina robust way.  During our research, we generated models for predicting values of performancecounters from values of tuning parameters of our benchmarks. Here, we providethe models themselves and describe the scripts we implemented for theirtraining. These data might benefit researchers who want to reproduce or buildon our research.

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures